In recent years, the evolving Internet of Things (IoT) technology has been widely used in
various industrial scenarios, whereby massive sensor data involving both normal data and …

Due to continued growth in both cyberattacks and network data size, organizations need to
develop advanced ways to keep their networks and data secure the dynamic nature of …

Anomaly target detection methods for hyperspectral images (HSI) often have the problems of
potential anomalies and noise contamination when representing background. Therefore, a …

Context: Anomaly detection is a crucial area engaging the attention of many researchers. It
is a process of finding an unusual point or pattern in a given dataset. It is useful in many real …

Feature selection is usually used as a preprocessing step for outlier detection to obtain
significant performance. There is little work on feature selection for outlier detection in …

Inspired by deep learning architectures, we introduce a multi-layer local discriminant
embedding algorithm that integrates feature selection as a main step to capture the most …

Abstract The One-Class Classification (OCC) exists in many real-world applications, such as
novelty detection, outlier detection, facial verification, and anomaly detection. SVDD is an …

The generalized likelihood ratio test (GLRT) is here combined with the nonparametric
approach to derive a new adaptive detector for subpixel targets in hyperspectral images …

Sparse least mean square algorithm is proposed for the DSTATCOM as an optimal current
harmonic extractor to cope with the intermittent nature of loadings. Sparse least mean …

Real-time anomaly detection for on-orbit satellites is crucial in the early identification of faults
to prevent further anomaly expansion. However, current anomaly detection methods for on …